Mobile notifications provide a unique mechanism for real-time information delivery systems to users in order to increase its effectiveness. However, real-time notification delivery to users via mobile phones does not always translate into users' awareness about the delivered information because these alerts might arrive at inappropriate times and situations. Moreover, notifications that demand users' attention at inopportune moments are more likely to have adverse effects and become a cause of potential disruption rather than proving beneficial to users. In order to address these challenges it is of paramount importance to devise intelligent notification mechanisms that monitor and learn users' behaviour for maximising their receptivity to the delivered information and adapt accordingly. The central goal of this dissertation is to build a framework for intelligent notifications that relies on the awareness of users' context and preferences. More specifically, we firstly investigate the impact of physical and cognitive contextual features on users' attentiveness and receptivity to notifications. Secondly, we construct and evaluate a series of models for predicting opportune moments to deliver notifications and mining users' notification delivery preferences in different situations. Finally, we design and evaluate a model for anticipating the right device notifications in cross-platform environments.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:715589 |
| Date | January 2017 |
| Creators | Mehrotra, Abhinav |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/7440/ |
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